Fatih University - Faculty of Engineering
Department of Electronics Engineering
Digital Image Processing Syllabus
I. COURSE DESCRIPTION
Course Code: EE 457
Course Name: Digital Image Processing
Course Objectives: The objectives of this course are to give a basic understanding on
images, operations can be performed on digital images such as image enhancement in spatial
and frequency domain, image restoration, image compression and morphological image
processing. MATLAB will be utilized to give the visual feeling of the theory learned in the
class.
Prerequisites: EE 353
Special Requirements: None
Instructor: Dr. Metin ARTIKLAR, E-mail: martiklar@fatih.edu.tr ,Office: EA-306
Office Hours: Monday: 13.00-14:00, Wednesday: 14:00-15:00, Thursday: 14:00-15:00
Textbook:
R., C., Gonzales and R., E., Woods, Digital Image Processing, Second Edition, Prentice Hall,
New Jersey, 2002.
References and Other Materials:
K., R., Castleman Malvino, Digital Image Processing, Prentice Hall, 1996.
II. COURSE CONTENTS
1. Introduction
What is digital image processing (DIP), fundamental steps in DIP, components of an image
processing system.
2. Digital Image Fundamentals
Elements of visual perception, light and electromagnetic spectrum, image sensing and
acquisition, image sampling and quantization, Some basic relationships between pixels,
linear and nonlinear operations.
3. Image Enhancement in the Spatial Domain
Some basic gray level transformations, histogram processing, enhancement using
arithmetic/Logic operations, Basics of spatial filtering, smoothing spatial filtering,
sharpening spatial filtering.
4. Image Enhancement in the Frequency Domain
Introduction to Fourier transform and the frequency domain, smoothing frequency domain
filters, sharpening frequency domain filters.
5. Image restoration
A model of the image degradation/restoration process, noise models, restoration in the
presence of noise, inverse filtering, minimum mean square error (Wiener) filtering,
geometric transformations.
6. Color Image Processing
Color fundamentals, color models, Pseudocolor image processing.
7. Image Compression
Fundamentals, image compression models, elements of information theory, error-free
compression, lossy-compression, image compression standards.
8. Morphological Image Processing
Preliminaries, dilation and erosion, opening and closing, hit-or-miss transformation, some
basic morphological algorithms, extension to gray level images.
III. EXAM DATES, GRADING POLICY AND ATTENDANCE
Exam Dates
Midterm Exam: April 25, 2003
Final Exams: June 16 – 27, 2003
Grading Policy
Homework and quizzes: 20 %
Midterm Exam:
30 %
Final Exam:
40 %
Others:
10 %
Attendance
Classroom and laboratory attendance are mandatory. Students, who are absent less than 10%
of the time will be rewarded with 5% towards their final grade. If the attendance is between
5% to 30% your grade will not be affected by your attendance record. If the attendance is
between 30% and 50%, your final grade will be cut by 5%. Students whom attend the class
less than 50% will automatically fail in the course. The same process will be applied to the
laboratory sections (if applicable) as well.
Honor Code: All work done on the exams will be done on your own and pledged. Students
may discuss homework concepts and approaches, but the work will be done by the individual.
Group or copied solutions are not permitted. Homework is considered pledged simply by its
receipt.
Late Work and Examinations: Normally, homework is due one week from the assigned
date, unless otherwise indicated or previous arrangements are made. Late homework will be
accepted due to sickness only if I am notified before the due date. Otherwise, for each day,
10% cut will applied to the grade. No make-up exam will be given for the midterm and the
final except under special circumstances. In the case of sickness, a report from a qualified
doctor has to be obtained and I should be notified either personally, by phone or by e-mail,
before or on the same day of absence in order for the student to qualify for the make-up.